model_rum.py

#!usrbinenv python
from __future__ import print_function

import tensorflow as tf
import cv2
import sys
sys.path.append(game)
import wrapped_flappy_bird as game
import random
import numpy as np
from collections import deque

GAME = 'bird' # the name of the game being played for log files
ACTIONS = 2 # number of valid actions
GAMMA = 0.99 # decay rate of past observations
OBSERVE = 100000. # timesteps to observe before training
EXPLORE = 2000000. # frames over which to anneal epsilon
FINAL_EPSILON = 0.0001 # final value of epsilon
INITIAL_EPSILON = 0.0001 # starting value of epsilon
REPLAY_MEMORY = 50000 # number of previous transitions to remember
BATCH = 32 # size of minibatch
FRAME_PER_ACTION = 1

def weight_variable(shape)
    initial = tf.truncated_normal(shape, stddev = 0.01)
    return tf.Variable(initial)

def bias_variable(shape)
    initial = tf.constant(0.01, shape = shape)
    return tf.Variable(initial)

def conv2d(x, W, stride)
    return tf.nn.conv2d(x, W, strides = [1, stride, stride, 1], padding = SAME)

def max_pool_2x2(x)
    return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = SAME)

def createNetwork()
    # network weights
    W_conv1 = weight_variable([8, 8, 4, 32])
    b_conv1 = bias_variable([32])

    W_conv2 = weight_variable([4, 4, 32, 64])
    b_conv2 = bias_variable([64])

    W_conv3 = weight_variable([3, 3, 64, 64])
    b_conv3 = bias_variable([64])

    W_fc1 = weight_variable([1600, 512])
    b_fc1 = bias_variable([512])

    W_fc2 = weight_variable([512, ACTIONS])
    b_fc2 = bias_variable([ACTIONS])

    # input layer
    s = tf.placeholder(float, [None, 80, 80, 4])

    # hidden layers
    h_conv1 = tf.nn.relu(conv2d(s, W_conv1, 4) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2, 2) + b_conv2)
    #h_pool2 = max_pool_2x2(h_conv2)

    h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3, 1) + b_conv3)
    #h_pool3 = max_pool_2x2(h_conv3)

    #h_pool3_flat = tf.reshape(h_pool3, [-1, 256])
    h_conv3_flat = tf.reshape(h_conv3, [-1, 1600])

    h_fc1 = tf.nn.relu(tf.matmul(h_conv3_flat, W_fc1) + b_fc1)

    # readout layer
    readout = tf.matmul(h_fc1, W_fc2) + b_fc2

    return s, readout, h_fc1

def trainNetwork(s, readout, h_fc1, sess)
    # define the cost function
    a = tf.placeholder(float, [None, ACTIONS])
    y = tf.placeholder(float, [None])
    readout_action = tf.reduce_sum(tf.multiply(readout, a), reduction_indices=1)
    cost = tf.reduce_mean(tf.square(y - readout_action))
    train_step = tf.train.AdamOptimizer(1e-6).minimize(cost)

    # open up a game state to communicate with emulator
    game_state = game.GameState()

    # store the previous observations in replay memory
    D = deque()

    # printing
    a_file = open(logs_ + GAME + readout.txt, 'w')
    h_file = open(logs_ + GAME + hidden.txt, 'w')

    # get the first state by doing nothing and preprocess the image to 80x80x4
    do_nothing = np.zeros(ACTIONS)
    do_nothing[0] = 1
    x_t, r_0, terminal = game_state.frame_step(do_nothing)
    x_t = cv2.cvtColor(cv2.resize(x_t, (80, 80)), cv2.COLOR_BGR2GRAY)
    ret, x_t = cv2.threshold(x_t,1,255,cv2.THRESH_BINARY)
    s_t = np.stack((x_t, x_t, x_t, x_t), axis=2)

    # saving and loading networks
    saver = tf.train.Saver()
    sess.run(tf.initialize_all_variables())
    checkpoint = tf.train.get_checkpoint_state(saved_networks)
    if checkpoint and checkpoint.model_checkpoint_path
        saver.restore(sess, checkpoint.model_checkpoint_path)
        print(Successfully loaded, checkpoint.model_checkpoint_path)
    else
        print(Could not find old network weights)

    # start training
    epsilon = INITIAL_EPSILON
    t = 0
    while flappy bird != angry bird
        # choose an action epsilon greedily
        readout_t = readout.eval(feed_dict={s  [s_t]})[0]
        a_t = np.zeros([ACTIONS])
        action_index = 0
        if t % FRAME_PER_ACTION == 0
            if random.random() = epsilon
                print(----------Random Action----------)
                action_index = random.randrange(ACTIONS)
                a_t[random.randrange(ACTIONS)] = 1
            else
                action_index = np.argmax(readout_t)
                a_t[action_index] = 1
        else
            a_t[0] = 1 # do nothing

        # scale down epsilon
        if epsilon  FINAL_EPSILON and t  OBSERVE
            epsilon -= (INITIAL_EPSILON - FINAL_EPSILON)  EXPLORE

        # run the selected action and observe next state and reward
        x_t1_colored, r_t, terminal = game_state.frame_step(a_t)
        x_t1 = cv2.cvtColor(cv2.resize(x_t1_colored, (80, 80)), cv2.COLOR_BGR2GRAY)
        ret, x_t1 = cv2.threshold(x_t1, 1, 255, cv2.THRESH_BINARY)
        x_t1 = np.reshape(x_t1, (80, 80, 1))
        #s_t1 = np.append(x_t1, s_t[,,1], axis = 2)
        s_t1 = np.append(x_t1, s_t[, , 3], axis=2)

        # store the transition in D
        D.append((s_t, a_t, r_t, s_t1, terminal))
        if len(D)  REPLAY_MEMORY
            D.popleft()

        # only train if done observing
        if t  OBSERVE
            # sample a minibatch to train on
            minibatch = random.sample(D, BATCH)

            # get the batch variables
            s_j_batch = [d[0] for d in minibatch]
            a_batch = [d[1] for d in minibatch]
            r_batch = [d[2] for d in minibatch]
            s_j1_batch = [d[3] for d in minibatch]

            y_batch = []
            readout_j1_batch = readout.eval(feed_dict = {s  s_j1_batch})
            for i in range(0, len(minibatch))
                terminal = minibatch[i][4]
                # if terminal, only equals reward
                if terminal
                    y_batch.append(r_batch[i])
                else
                    y_batch.append(r_batch[i] + GAMMA  np.max(readout_j1_batch[i]))

            # perform gradient step
            train_step.run(feed_dict = {
                y  y_batch,
                a  a_batch,
                s  s_j_batch}
            )

        # update the old values
        s_t = s_t1
        t += 1

        # save progress every 10000 iterations
        if t % 10000 == 0
            saver.save(sess, 'saved_networks' + GAME + '-dqn', global_step = t)

        # print info
        state = 
        if t = OBSERVE
            state = observe
        elif t  OBSERVE and t = OBSERVE + EXPLORE
            state = explore
        else
            state = train

        print(TIMESTEP, t,  STATE, state, 
             EPSILON, epsilon,  ACTION, action_index,  REWARD, r_t, 
             Q_MAX %e % np.max(readout_t))
        # write info to files
        '''
        if t % 10000 = 100
            a_file.write(,.join([str(x) for x in readout_t]) + 'n')
            h_file.write(,.join([str(x) for x in h_fc1.eval(feed_dict={s[s_t]})[0]]) + 'n')
            cv2.imwrite(logs_tetrisframe + str(t) + .png, x_t1)
        '''

def playGame()
    sess = tf.InteractiveSession()
    s, readout, h_fc1 = createNetwork()
    trainNetwork(s, readout, h_fc1, sess)

def main()
    playGame()

if __name__ == __main__
    main()